Head motion-related sensory signals are transformed bysecond-order vestibular neurons (2°VNs) into appropriate commands for retinal image stabilization during body motion. In frogs, these 2°VNs form two distinct subpopulations that have either tonic or highly phasic intrinsic properties, essentially compatible with low-pass and bandpass filter characteristics, respectively. In the present study, physiological data on cellular properties of 2°VNs of the grass frog (Rana temporaria) have been used to construct conductance-based spiking cellular models that were fine-tuned by fitting to recorded spike-frequency data. The results of this approach suggest that low-threshold, voltage-dependent potassium channels in phasic and spike-dependent potassium channels in tonic 2°VNs are important contributors to the differential, yet complementary response characteristics of the two vestibular subtypes. Extension of the cellular model with conductance-based synapses allowed simulation of afferent excitation and evaluation of the emerging properties of local feedforward inhibitory circuits. This approach revealed the relative contributions of intrinsic and synaptic factors on afferent signal processing in phasic 2°VNs. Additional extension of the single-cell model to a population model allowed testing under more natural conditions including asynchronous afferent labyrinthine input and synaptic noise. This latter approach indicated that the feedforward inhibition from the local inhibitory network acts as a high-pass filter, which reinforces the impact of the intrinsic membrane properties of phasic 2°VNs on peak response amplitude and timing. Thus, the combination of cellular and network properties enables phasic 2°VNs to work as a noise-resistant detector, suitable for central processing of short-duration vestibular signals. © 2011 the authors.
Rössert, C., Moore, L. E., Straka, H., & Glasauer, S. (2011). Cellular and network contributions to vestibular signal processing: Impact of ion conductances, synaptic inhibition, and noise. Journal of Neuroscience, 31(23), 8359–8372. https://doi.org/10.1523/JNEUROSCI.6161-10.2011